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AI analysis for Chest x-ray
AI analysis for Mammography
AI analysis for Tissue Slides
Chest radiography is one of the most basic and fundamental diagnostic tests used in medicine, accounting for 25% of the annual total numbers of diagnostic imaging procedures.1 It has been shown that radiologic information changed clinical practice in more than 60% of those who received chest radiography.2,3 Unfortunately, miss rates for proper interpretation of chest radiographs go as high as 30% even for experts,4,5 leading to increased mortality from treatable diseases.6 Moreover, the interpretive performance of chest radiographs differ significantly between specialists and non-specialists, up to 30%.7-9 Additionally, 10% of chest radiographs are reported to be held back for 30 days until the final report is issued, and only 60% of radiographs are reported by radiologists due to overflowing number of cases to interpret.10 Improvement in the radiology workflow and efficiency can greatly alleviate the burden.
Developed using Lunit’s cutting-edge deep learning technology,11-14 which has been validated through publications in numerous major publications such as Radiology,15-16 Scientific Reports,17 Clinical Infectious Diseases18 and more, Lunit INSIGHT CXR 3 accurately detects 10 of the most common findings in a chest x-ray, which includes atelectasis, calcification, cardiomegaly, consolidation, fibrosis, mediastinal widening, nodule, pleural effusion, pneumoperitoneum, and pneumothorax. The AI solution generates (1) location information of detected lesions in the form of heatmaps, (2) abnormality scores reflecting the probability that the detected lesion is abnormal, and (3) an AI “case report” that summarizes the analysis result by each finding. The solution is indicated to be directly involved in the primary interpretation process of radiologists or clinicians.
Primary value proposition
- Prevent difficult cases of chest abnormalities from being missed upon reading chest radiographs.
- Help physicians make early diagnosis of chest abnormalities in chest radiographs.
- Increases workflow efficiency in interpretation through decreasing reading time by 34%.
- Trained to individually detect and locate 10 different radiologic findings
- The user can customize detectable findings and its visualization method according to user clinical environment
- Automatically generates case report which includes analysis of each radiologic findings and its location information
- Provides TB screening AI score to identify tuberculosis on the chest radiograph
Training & Validation
- Trained with a large-scale (>200,000 cases), high-quality (clinically/CT-proven cases) training set.
- Demonstrated to perform at a standalone accuracy of 97-99% in ROC AUC.19
- Certified with CE Mark and approved by Korea MFDS.
- Currently in preparation for regulatory approval in various markets worldwide, including FDA.
Case 1 : Subtle consolidation, diagnosed as pneumonia, is properly detected in the right lower zone, with an abnormality score of 29%.
Case 2 : Multiple subsegmental atelectasis is in both lungs with pleural effusion.
Case 3 : Free air is present under the right diaphragm, pneumoperitoneum.